{p} About {p} 10 years ago, I took on the task to {p} teach global development to Swedish undergraduate students. {p} That was after having spent about {p} 20 years together with African institutions studying hunger in Africa, {p} so I was sort of {p} expected to know a little about the world. And {p} I started in our medical university, Karolinska Institute, an undergraduate {p} course called Global Health. {p} But when you get that opportunity, you get a little nervous. I thought, {p} these students coming to us actually have the highest grade you can get in Swedish college systems so, I thought, maybe they know everything I'm going to teach them about. {p} So I did a pre-test {p} when they came. And one of the questions {p} from which I learned a lot was this one: "Which country has the highest child mortality {p} of these five pairs?" {p}
And I put them together, so that in each pair of country, one has {p} twice the child mortality of the other. {p} And this means {p} that {p} it's much bigger a difference than the uncertainty of the data. {p} I won't put you at a test here, but it's Turkey, which is highest there, Poland, {p} Russia, Pakistan {p} and South Africa. {p} And these were the results of the Swedish students. I did it so I got the confidence interval, which is {p} pretty narrow, {p} and I got happy, of course: {p} a 1.8 right answer out of five possible. {p} That means that there was a place for a professor of international health {p} {LG} and for my course.
But one {p} late night, when I {p} was compiling the report I really realized my discovery. {p} I have shown {p} that Swedish top students {p} know {p} statistically significantly less about the world than the chimpanzees. {p} {LG} {p} Because the chimpanzee would score {p} half right {p} if I gave them two bananas with Sri Lanka and Turkey. They would be right half of the cases. {p}
But the students are not there. {p} The problem for me {p} was not ignorance; it was preconceived ideas. {p}
I did also an {p} unethical study of the professors of the Karolinska Institute {LG} that hands out the Nobel Prize in Medicine, and they are on par with the chimpanzee there.{LG} This {p} is where I realized that there was really a need to communicate, because the data {p} of what's happening in the world and the child health of every country is very well aware. {p}
We did this software which displays it like this: {p} every bubble here is a country. {p} This country over here is China. This is India. {p} The {p} size of the bubble {p} is the population, {p} and on this axis here I put {p} fertility rate. {p} Because my students, {p} what they said {p} when they looked upon the world, and I asked them, "What do you really {p} think about the world?" {p} Well, I first discovered that the textbook {p} was Tintin, mainly. {p} {LG} And {p} they said, "The world is still 'we' and 'them.' {p} And we is Western world and them {p} is Third World." {p} "And what do you mean with Western world?" I said. {p} "Well, {p} that's long life and small family, and Third World {p} is short life and large family." {p}
So this is what I could display here. {p} I put fertility rate here: {p} number of children per woman:one, two, three, four, up to about eight children per woman. {p} We have very good data {p} since 1962 1960 about on the size of families in all countries. The error margin is narrow. Here I put life expectancy at birth, {p} from 30 years in some countries {p} up to about 70 years.And 1962, {p} there was really a group of countries here that was {p} industrialized countries, {p} and they had {p} small families {p} and long lives. {p} And these were the developing countries: they had {p} large families {p} and they had relatively short lives. {p} Now what has happened since 1962? {p} We want to see the change. {p} Are the students {p} right? Is it still two types of countries? {p} Or have these developing countries got smaller families and they live here? {p} Or have they got longer lives and live up there? {p}
Let's see. We stopped the world then. This is all U.N. statistics that have been available. Here we go. Can you see there? {p} It's China there, moving against better health there, improving there. {p} All the green Latin American countries {p} are moving towards smaller families. {p} Your yellow ones here are the Arabic countries, and they get larger families, {p} but they no, longer life, {p} but not larger families. The Africans are the green down here. They still remain here. {p} This {p} is India. Indonesia's moving on pretty fast. {LG} And in the '80s here, {p} you have Bangladesh still among the African countries there. {p} But now, Bangladesh it's a miracle that happens in the '80s: {p} the imams start to promote family planning. They move up into that corner. {p} And in {p} '90s, {p} we have the terrible HIV epidemic {p} that takes down {p} the life expectancy of the African countries {p} and all the rest of them {p} move up into the corner, {p} where we have long lives {p} and small family, {p} and we {p} have a completely {p} new world.{NS}
Let me make a comparison directly between the United States of America and {p} Vietnam.1964: {p} America had small families and long life; {p} Vietnam had large families {p} and short lives. {p} And this is what happens: {p} the data {p} during the war {p} indicate {p} that {p} even with all the death,there was an improvement of life expectancy. {p} By the end of the year, the family planning started in Vietnam and they went for smaller families. {p} And the United States up there is getting for longer life, keeping family size. {p} And in the '80s now, {p} they give up {p} communist planning and they go for market economy, and it moves faster even than social life. {p} And today, {p} we have in Vietnam {p} the same {p} life expectancy {p} and the same family size {p} here in Vietnam, 2003, {p} as in United States, {p} 1974, by the end of the war. {p} I think we {p} all if we don't {p} look in the data we underestimate the tremendous change in Asia, {p} which was in social change {p} before we saw {p} the economical change. {p}
Let's move over to another way here {p} in which we could {p} display {p} the distribution {p} in the world of the income. {p} This is the world distribution of income {p} of people. {p} One dollar, {p} 10 dollars {p} or 100 dollars per day. {p} There's no gap {p} between rich and poor any longer. This is a myth.There's {p} a little hump here. {p} But {p} there are people all the way. {p} And {p} if we look where the income {p} ends up {p} the {p} income this is 100 percent the world's annual income. {p} And {p} the richest 20 percent, {p} they take out of that {p} about {p} 74 percent. {p} And {p} the poorest 20 percent, {p} they take {p} about {p} two percent. {p} And this shows that the concept of developing countries is extremely doubtful. {p} We think about aid, {p} like these people here {p} giving aid to {p} these people here. {p} But in the middle, we have most the world population, {p} and {p} they have {p} now 24 percent of the income.
We heard it in other forms. {p} And who are {p} these? {p} Where are the different countries? {p} I can show you Africa. {p} This is {p} Africa. {p} 10 percent the world population, most in poverty. {p} This is OECD. The {p} rich country. The country club of the U.N. {p} And they are over here on this side. Quite an overlap {p} between Africa {p} and OECD. {p} And this is Latin America. It has everything on this {p} Earth, from the poorest {p} to the richest, {p} in Latin America. {p} And {p} on {p} top of that, {p} we can put East Europe, {p} we can put East Asia, {p} and we put South Asia. {p} And how did it look like {p} if we go back in time, {p} to about {p} 1970? {p} Then there was {p} more of a {p} hump. {p} And we have {p} most who {p} lived in absolute poverty were Asians. {p} The problem in the world was the poverty in Asia. {p} And if I now {p} let the world move forward, {p} you will see that while population increase, {p} there are hundreds of millions in Asia getting out of poverty {p} and some others getting into poverty, {p} and this is the pattern we have today. {p} And the best projection from the World Bank {p} is that this will happen, {p} and we will not have a divided world. {p} We'll have most people in the middle.
Of course it's a logarithmic scale here, {p} but {p} our concept of economy {p} is growth with percent. {p} We look upon it {p} as {p} a possibility {p} of {p} percentile increase. If I change {p} this, {p} and I take GDP {p} per capita instead of family income, {p} and I turn these {p} individual {p} data {p} into {p} regional data of gross domestic product, {p} and I take the regions down here, the size of the bubble is still the population. {p} And you have the OECD there, and you have sub-Saharan Africa there, {p} and we take off {p} the Arab states there, coming both from Africa and from Asia, {p} and we put them separately, {p} and we can expand this {p} axis, {p} and I can give it a new dimension here, {p} by {p} adding the social values there, child survival. Now I have money on that axis, {p} and I have the possibility of children to survive there. In some countries, 99.7 percent of children {p} survive to five years of age; {p} others, {p} only 70. {p} And here it seems there {p} is a gap between {p} OECD, Latin America, {p} East Europe, {p} East Asia, Arab states, {p} South Asia {p} and {p} sub-Saharan Africa.The {p} linearity is very strong {p} between child survival {p} and {p} money. {p}
But let me split {p} sub-Saharan Africa. {p} Health is there {p} and {p} better {p} health is up there. {p} I can go here {p} and I can split sub-Saharan Africa {p} into its countries. {p} And when it burst, the size of its {p} country bubble {p} is the size of the population. Sierra Leone {p} down there. {p} Mauritius is up there. {p} Mauritius was the first country {p} to get away with trade barriers, and they could sell their sugar {p} they could sell {p} their textiles {p} on equal {p} terms as the people in Europe and North America. {p}
There's a huge difference between Africa. And Ghana is here in the middle. {p} In Sierra Leone, humanitarian aid. {p} Here {p} in Uganda, {p} development aid. {p} Here, time to invest; {p} there, you can go for a holiday. {p} It's a tremendous variation within Africa {p} which we rarely often make {p} that it's equal everything. {p} I can split South Asia here. {p} India's the big bubble in the middle. {p} But a huge difference between {p} Afghanistan {p} and {p} Sri Lanka. I {p} can split Arab states. How are they? {p} Same climate, {p} same culture, same religion {p} huge difference. {p} Even between neighbors.Yemen, civil war. {p} United Arab Emirate, money {p} which was quite equally and well used. {p} Not {p} as the myth is. {p} And that {p} includes all the children of the {p} foreign workers who are in the country. {p} Data is often better than you think. {p} Many people say data is bad. There is an uncertainty margin, {p} but we can see the difference here: Cambodia, {p} Singapore. The differences are much {p} bigger than the weakness of the data. {p} East Europe: {p} Soviet {p} economy {p} for a long time, but they come out {p} after 10 years very, very differently. {p} And there is Latin America. {p} Today, we don't have to go to Cuba to find a healthy country in Latin America.Chile will have a lower child mortality than Cuba within some few years from now. {p} And here we have high-income countries in the OECD. {p}
And we get {p} the whole pattern here {p} of {p} the world, {p} which is more or less like {p} this. {p} And {p} if we {p} look at it, {p} how it looks the world, {p} in 1960, {p} it starts to move. 1960. This is Mao Tse-tung. He brought health {p} to China. {p} And then he died. And then Deng Xiaoping came and brought money to China, {p} and brought them into the mainstream again. {p} And we have seen how countries move in {p} different directions like {p} this, {p} so {p} it's {p} sort of {p} difficult to get an {p} example country which shows the pattern of the world. {p} But I would like {p} to bring you back {p} to {p} about here {p} at {p} 1960. {p} I would like {p} to {p} compare {p} South Korea, {p} which is this {p} one, {p} with Brazil, {p} which is this one. {p} The label went away for me here. {p} And I would like to compare Uganda, {p} which is there. {p} And {p} I can run it {p} forward, {p} like {p} this. {p} And you can see {p} how {p} South Korea is making a very, very fast advancement, {p} whereas Brazil {p} is much slower. {p}
And if we move back {p} again, here, {p} and we put on {p} trails on them, like this, {p} you can see again {p} that {p} the speed of development is very, very different, {p} and {p} the countries {p} are moving {p} more or less in the same rate {p} as money {p} and health, but it seems you can move much faster if you are healthy first {p} than if you are wealthy first. {p} And {p} to show that, you can put on the way of United Arab Emirate. They came from here, {p} a mineral country. {p} They cached all the oil; {p} they got all the money; {p} but {p} health cannot {p} be bought at the supermarket. {p} You have to invest in health. You have to get kids into schooling. You have to train health staff. {p} You have to educate the population. {p} And Sheikh Sayed did that in a fairly good way. {p} In spite of falling oil prices, {p} he brought this country up here. {p} So we've got a much more {p} mainstream appearance of the world, where all countries {p} tend to use their money {p} better {p} than they used in the past. {p} Now, {p} this is, more {p} or {p} less, {p} if you look at the {p} average {p} data {p} of the countries {p} they are like {p} this. {p}
Now {p} that's {p} dangerous, {p} to use average data, {p} because {p} there is such a lot of difference within countries. {p} So if I go {p} and look here, {p} we can see that {p} Uganda {p} today {p} is where South Korea was 1960. If I split {p} Uganda, {p} there's quite a difference {p} within Uganda. {p} These are the quintiles of Uganda. The richest 20 percent of Ugandans are there. {p} The poorest are down there. {p} If I split South Africa, {p} it's like this. And if I go down {p} and look at {p} Niger, {p} where there was such a terrible famine, {p} lastly, {p} it's like this. {p} The 20 percent poorest of Niger is out here, {p} and the 20 percent {p} richest of South Africa is there, {p} and {p} yet {p} we tend to discuss {p} on what solutions there should be in Africa. {p} Everything in this world exists in Africa. And {p} you can't discuss {p} universal access to HIV {p} [medicine] {p} for that quintile up here {p} with the same strategy {p} as down here. {p} The improvement of the world must be highly contextualized, {p} and it's not {p} relevant to have it {p} on regional level. We must be much more detailed. {p} We find that students get very excited when they can use this. {p}
And even more policy makers and the corporate sectors {p} would like to see {p} how the world is {p} changing. Now, {p} why doesn't this take {p} place? {p} Why are we not using the data we have? We have data in the United Nations, {p} in the national statistical agencies {p} and in universities and other non-governmental organizations. Because the data is hidden down in the databases.And {p} the public is there, {p} and the Internet is there, {p} but we have still not used it effectively. {p}
All that information we saw changing in the world {p} does not include publicly-funded statistics. There are some web pages like {p} this, {p} you know, {p} but {p} they take some {p} nourishment down from the databases, but people put prices on them, {p} stupid passwords {p} and boring {p} statistics. {LG} {NS}
And this won't work. So what is needed? We have the databases. It's not the new database you need. {p} We have wonderful design tools, and {p} more and more are added up here. {p} So we started a nonprofit venture {p} which we called {p} linking data to design we call it Gapminder, from the London underground, where they warn you, "mind the gap." {p} So we thought Gapminder was appropriate. {p} And we started to write software which could {p} link {p} the data like this. {p} And {p} it wasn't {p} that difficult. {p} It {p} took some person years, {p} and we have produced {p} animations. {p} You can take a data set and put it there. {p} We are {p} liberating {p} U.N. data, {p} some few U.N. organization. {p}
Some countries {p} accept {p} that their databases can go out {p} on the world, {p} but what we really need {p} is, of course, a search function. {p} A search function {p} where we can copy the data {p} up to a searchable format {p} and get it out in the world. {p} And what do we hear when we go around?I've done anthropology on the main statistical units. {p} Everyone says, "It's impossible. {p} This can't be done. Our information is so peculiar in detail, {p} so that cannot be searched {p} as others can be searched. {p} We cannot give the data {p} free to the students, free to the entrepreneurs of the world." {p} But this is what we would like to see, isn't it? {p} The publicly-funded data is down here. {p} And we would like {p} flowers to grow out on the Net. {p} And one of the crucial points {p} is to make them searchable, {p} and then people can use the different {p} design tool {p} to animate it there. {p} And I have a pretty good news for you. {p} I have a good news that the present, {p} new Head of U.N. Statistics, he doesn't say {p} it's impossible. He only says, "We can't {p} do it."{LG} {p} And {p} that's a quite clever guy, {p} huh? {LG} {p}
So {p} we can see a lot happening {p} in data {p} in the coming years. {p} We will be able to look at income distributions {p} in completely new ways. {p} This is the income distribution of {p} China, {p} 1970. {p} the {p} income distribution {p} of the United States, {p} 1970. {p} Almost no overlap. {p} Almost no overlap. {p} And what has happened? {p} What has happened is this: {p} that China is growing, it's not so equal any longer, {p} and it's appearing here, {p} overlooking the United States. {p} Almost like a ghost, isn't it, huh? {p} {LG} {p}
It's {p} pretty {p} scary. {p} But I think it's very important to have all this {p} information. {p} We {p} need really {p} to see it. {p} And instead of looking at {p} this, {p} I would like to {p} end up {p} by showing {p} the Internet users per 1,000. {p} In this software, we access about 500 variables from all the countries {p} quite easily. {p} It takes some {p} time {p} to {p} change for this, {p} but {p} on the axises, {p} you can quite easily {p} get any variable you would like to have. {p} And {p} the {p} thing would be {p} to get {p} up {p} the databases free, {p} to get them searchable, {p} and with a second click, {p} to get them {p} into the graphic formats, {p} where you can instantly {p} understand them. Now, statisticians doesn't like it, {p} because they say {p} that {p} this will not {p} show {p} the reality; we have to have statistical, analytical methods. But this is {p} hypothesis-generating. {p}
I {p} end now {p} with the world. {p} There, the Internet is coming. The number of Internet users are going up like this. {p} This is the GDP per capita. {p} And it's a new technology coming in, {p} but then amazingly, {p} how well it fits {p} to the economy of the countries. {p} That's why the 100 dollar computer will be so important. {p} But it's a nice tendency. {p} It's as if the world is flattening off, isn't it? {p} These countries are lifting more than the economy {p} and will be very interesting {p} to follow this over the year, {p} as I would like you to be able to do {p} with all the publicly funded data. Thank you very much. {NS} {p}